This package is a r-ggplot2 extension that provides flipped components:

1. horizontal versions of r-ggplot2 stats and r-ggplot2 geoms;

2. vertical versions of r-ggplot2 positions.

This package provides an exact Goodness-of-Fit test for multinomial data with fixed probabilities. It can be used to determine whether a set of counts fits a given expected ratio. To see whether a set of observed counts fits an expectation, one can examine all possible outcomes with xmulti() or a random sample of them with xmonte() and find the probability of an observation deviating from the expectation by at least as much as the observed. As a measure of deviation from the expected, one can use the log-likelihood ratio, the multinomial probability, or the classic chi-square statistic. A histogram of the test statistic can also be plotted and compared with the asymptotic curve.

This package provides a non-linear model, termed ACME, that reflects a parsimonious biological model for allelic contributions of cis-acting eQTLs. With non-linear least-squares algorithm the maximum likelihood parameters can be estimated. The ACME model provides interpretable effect size estimates and p-values with well controlled Type-I error.

This package provides functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution.

This package provides a collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in http://pj.freefaculty.org/guides. The package includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette rockchalk offers a fairly comprehensive overview.

This package provides two convenience functions assert() and test_pkg() to facilitate testing R packages.

This package implements several Approximate Bayesian Computation (ABC) algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.

This package provides a unified parallelization framework for multiple backends. This package is designed for internal package and interactive usage. The main operation is parallel mapping over lists. It supports local, multicore, mpi and BatchJobs mode. It allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation.

This package provides a set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).

This package contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

This package provides algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette.

This package provides a light weight implementation of the standard distribution functions for the inverse gamma distribution, wrapping those for the gamma distribution in the stats package.

This package extends several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.

This package performs optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms (Nelder-Mead, BFGS, CG, L-BFGS-B and SANN) underlying optim().

Easily and flexibly insert Font Awesome icons into R Markdown documents and Shiny apps. These icons can be inserted into HTML content through inline SVG tags or i tags. There is also a utility function for exporting Font Awesome icons as PNG images for those situations where raster graphics are needed.

This package provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment.

This package represents a collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

This package extends the out of memory vectors of ff with statistical functions and other utilities to ease their usage.

This package provides ggplot2 geoms filled with various patterns. It includes a patterned version of every ggplot2 geom that has a region that can be filled with a pattern. It provides a suite of ggplot2 aesthetics and scales for controlling pattern appearances. It supports over a dozen builtin patterns (every pattern implemented by gridpattern) as well as allowing custom user-defined patterns.

This package provides tools for creating detailed dataframes for common statistical approaches and tests. These include parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for ggstatsplot.

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces tailor objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the tidymodels framework.

This package lets you build complex Structured Query Language (SQL) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either American National Standards Institute (ANSI) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.

This package provides a native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library.

A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.